
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase     :	List[Any]       			=  {
    'configuration_blenderbot_small': [
        'BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotSmallConfig',
        'BlenderbotSmallOnnxConfig',
    ],
    'tokenization_blenderbot_small': ['BlenderbotSmallTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Dict       			=  ['BlenderbotSmallTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	List[Any]       			=  [
        'BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BlenderbotSmallForCausalLM',
        'BlenderbotSmallForConditionalGeneration',
        'BlenderbotSmallModel',
        'BlenderbotSmallPreTrainedModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Dict       			=  [
        'TFBlenderbotSmallForConditionalGeneration',
        'TFBlenderbotSmallModel',
        'TFBlenderbotSmallPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	str       			=  [
        'FlaxBlenderbotSmallForConditionalGeneration',
        'FlaxBlenderbotSmallModel',
        'FlaxBlenderbotSmallPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_blenderbot_small import (
        BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BlenderbotSmallConfig,
        BlenderbotSmallOnnxConfig,
    )
    from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_blenderbot_small import (
            BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
            BlenderbotSmallForCausalLM,
            BlenderbotSmallForConditionalGeneration,
            BlenderbotSmallModel,
            BlenderbotSmallPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_blenderbot_small import (
            TFBlenderbotSmallForConditionalGeneration,
            TFBlenderbotSmallModel,
            TFBlenderbotSmallPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_blenderbot_small import (
            FlaxBlenderbotSmallForConditionalGeneration,
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallPreTrainedModel,
        )

else:
    import sys

    __lowerCAmelCase     :	int       			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


__lowerCAmelCase     :	Optional[Any]       			=  'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			_ask_options(
      """In which compute environment are you running?""",	["""This machine""", """AWS (Amazon SageMaker)"""],	_convert_compute_environment,	)
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    __magic_name__								=			get_sagemaker_input()
  else:
    __magic_name__								=			get_cluster_input()
  return config

def a__       (			A_=None   ):






  '''simple docstring'''





  if subparsers is not None:
    __magic_name__								=			subparsers.add_parser("""config""",	description=A_   )
  else:
    __magic_name__								=			argparse.ArgumentParser("""Accelerate config command""",	description=A_   )

  parser.add_argument(
      """--config_file""",	default=A_,	help=(
          """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
          """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
          """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
          """with 'huggingface'."""
      ),	)

  if subparsers is not None:
    parser.set_defaults(func=A_   )
  return parser

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			get_user_input()
  if args.config_file is not None:
    __magic_name__								=			args.config_file
  else:
    if not os.path.isdir(A_   ):
      os.makedirs(A_   )
    __magic_name__								=			default_yaml_config_file

  if config_file.endswith(""".json"""   ):
    config.to_json_file(A_   )
  else:
    config.to_yaml_file(A_   )
  print(f'''accelerate configuration saved at {config_file}'''   )

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			config_command_parser()
  __magic_name__								=			parser.parse_args()
  config_command(A_   )


if __name__ == "__main__":
    main()

